The State of the Art

• Random Utility Models
  • Statistically Efficient
  • Easily Generalizable
  • Policy-Brittle
• Dynamic Programming
  • Strongly Rational
  • Computationally Expensive
  • Ad hoc

The One Agent Problem

• Find the most profitable spot to fish
• Constraints:
  • No biomass information
  • Environment changes over time
• Subproblems:
  • How to explore
  • Explore-Exploit Tradeoff

One Agent sample run

Explore-Exploit

• How to explore
  • Tow at a nearby cell from subjective best
  • Random Hill-Climbing
• When to explore
  • Stochastically choose to explore next trip with probability \(p\)
  • Adjust \(p\) if exploration is often (un)succesful
• Why not more nuanced bandit algorithms?

Explore-Exploit-Imitate

• Other boats consume biomass
• You can use other boats information
• How to imitate?
• With probability \(p\) explore, with probability \(i\) imitate a competitor otherwise exploit

Two Agents sample run

Many Agents

Oil Prices

Fish the Line (part 1)

Fish the Line (part 2)

Target Switching

Gear Selection

Simulating Quotas

• TAC: Total Allowable Catch
• ITQ: Individual Tradeable Quota

Single Species TAC vs ITQ (mileage)

Single Species TAC vs ITQ (catchability)

North-South world

Location choices

ITQ incentivates geography

Optimise Policies

Optimal TAC

Optimal ITQ

Well-Mixed World

OSMOSE

California

State of California

• Biomass properly distributed
• Gear and boats parametrized
• Quotas correct, trading simplified
• Not fitted to logbook data yet
• Weather and fish movement missing

Winners and Losers

Winners and Losers (with policy)